Non-Contact Video-Based Neonatal Respiratory Monitoring
Respiratory rate (RR) has been shown to be a reliable predictor of cardio-pulmonary deterioration, but standard RR monitoring methods in the neonatal intensive care units (NICU) with contact leads have been related to iatrogenic complications. Video-based monitoring is a potential non-contact system...
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doaj-788c98b365004ae0a5410744650421112021-04-02T11:10:35ZengMDPI AGChildren2227-90672020-10-01717117110.3390/children7100171Non-Contact Video-Based Neonatal Respiratory MonitoringScott L. Rossol0Jeffrey K. Yang1Caroline Toney-Noland2Janine Bergin3Chandan Basavaraju4Pavan Kumar5Henry C. Lee6Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USADepartment of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USADepartment of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USADepartment of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USACocoonCam, Sunnyvale, CA 94089, USACocoonCam, Sunnyvale, CA 94089, USADepartment of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USARespiratory rate (RR) has been shown to be a reliable predictor of cardio-pulmonary deterioration, but standard RR monitoring methods in the neonatal intensive care units (NICU) with contact leads have been related to iatrogenic complications. Video-based monitoring is a potential non-contact system that could improve patient care. This iterative design study developed a novel algorithm that produced RR from footage analyzed from stable NICU patients in open cribs with corrected gestational ages ranging from 33 to 40 weeks. The final algorithm used a proprietary technique of micromotion and stationarity detection (MSD) to model background noise to be able to amplify and record respiratory motions. We found significant correlation—<i>r</i> equals 0.948 (<i>p</i> value of 0.001)—between MSD and the current hospital standard, electrocardiogram impedance pneumography. Our video-based system showed a bias of negative 1.3 breaths and root mean square error of 6.36 breaths per minute compared to standard continuous monitoring. Further work is needed to evaluate the ability of video-based monitors to observe clinical changes in a larger population of patients over extended periods of time.https://www.mdpi.com/2227-9067/7/10/171neonatal monitoringrespiratory rateclinical alarmsvideo recordingbiomedical technology |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Scott L. Rossol Jeffrey K. Yang Caroline Toney-Noland Janine Bergin Chandan Basavaraju Pavan Kumar Henry C. Lee |
spellingShingle |
Scott L. Rossol Jeffrey K. Yang Caroline Toney-Noland Janine Bergin Chandan Basavaraju Pavan Kumar Henry C. Lee Non-Contact Video-Based Neonatal Respiratory Monitoring Children neonatal monitoring respiratory rate clinical alarms video recording biomedical technology |
author_facet |
Scott L. Rossol Jeffrey K. Yang Caroline Toney-Noland Janine Bergin Chandan Basavaraju Pavan Kumar Henry C. Lee |
author_sort |
Scott L. Rossol |
title |
Non-Contact Video-Based Neonatal Respiratory Monitoring |
title_short |
Non-Contact Video-Based Neonatal Respiratory Monitoring |
title_full |
Non-Contact Video-Based Neonatal Respiratory Monitoring |
title_fullStr |
Non-Contact Video-Based Neonatal Respiratory Monitoring |
title_full_unstemmed |
Non-Contact Video-Based Neonatal Respiratory Monitoring |
title_sort |
non-contact video-based neonatal respiratory monitoring |
publisher |
MDPI AG |
series |
Children |
issn |
2227-9067 |
publishDate |
2020-10-01 |
description |
Respiratory rate (RR) has been shown to be a reliable predictor of cardio-pulmonary deterioration, but standard RR monitoring methods in the neonatal intensive care units (NICU) with contact leads have been related to iatrogenic complications. Video-based monitoring is a potential non-contact system that could improve patient care. This iterative design study developed a novel algorithm that produced RR from footage analyzed from stable NICU patients in open cribs with corrected gestational ages ranging from 33 to 40 weeks. The final algorithm used a proprietary technique of micromotion and stationarity detection (MSD) to model background noise to be able to amplify and record respiratory motions. We found significant correlation—<i>r</i> equals 0.948 (<i>p</i> value of 0.001)—between MSD and the current hospital standard, electrocardiogram impedance pneumography. Our video-based system showed a bias of negative 1.3 breaths and root mean square error of 6.36 breaths per minute compared to standard continuous monitoring. Further work is needed to evaluate the ability of video-based monitors to observe clinical changes in a larger population of patients over extended periods of time. |
topic |
neonatal monitoring respiratory rate clinical alarms video recording biomedical technology |
url |
https://www.mdpi.com/2227-9067/7/10/171 |
work_keys_str_mv |
AT scottlrossol noncontactvideobasedneonatalrespiratorymonitoring AT jeffreykyang noncontactvideobasedneonatalrespiratorymonitoring AT carolinetoneynoland noncontactvideobasedneonatalrespiratorymonitoring AT janinebergin noncontactvideobasedneonatalrespiratorymonitoring AT chandanbasavaraju noncontactvideobasedneonatalrespiratorymonitoring AT pavankumar noncontactvideobasedneonatalrespiratorymonitoring AT henryclee noncontactvideobasedneonatalrespiratorymonitoring |
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